{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebd66700",
   "metadata": {},
   "source": [
    "## Demo_SHAP\n",
    "This is a demo for visualizing the Shapely Value of a Neuron Network\n",
    "\n",
    "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b950f4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "! python ../../attack/badnet.py --save_folder_name badnet_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f81f973",
   "metadata": {},
   "source": [
    "or run the following command in your terminal\n",
    "\n",
    "```python attack/badnet.py --save_folder_name badnet_demo```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bd9f5a",
   "metadata": {},
   "source": [
    "### Step 1: Import modules and set arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71b7087b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import yaml\n",
    "import torch\n",
    "import shap\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "sys.path.append(\"../../\")\n",
    "sys.path.append(os.getcwd())\n",
    "from visual_utils import *\n",
    "from utils.aggregate_block.dataset_and_transform_generate import (\n",
    "    get_transform,\n",
    "    get_dataset_denormalization,\n",
    ")\n",
    "from utils.aggregate_block.fix_random import fix_random\n",
    "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n",
    "from utils.save_load_attack import load_attack_result\n",
    "from utils.defense_utils.dbd.model.utils import (\n",
    "    get_network_dbd,\n",
    "    load_state,\n",
    "    get_criterion,\n",
    "    get_optimizer,\n",
    "    get_scheduler,\n",
    ")\n",
    "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fb719c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Basic setting: args\n",
    "args = get_args(True)\n",
    "\n",
    "########## For Demo Only ##########\n",
    "args.yaml_path = \"../../\"+args.yaml_path\n",
    "args.result_file_attack = \"badnet_demo\"\n",
    "######## End For Demo Only ##########\n",
    "\n",
    "with open(args.yaml_path, \"r\") as stream:\n",
    "    config = yaml.safe_load(stream)\n",
    "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n",
    "args.__dict__ = config\n",
    "args = preprocess_args(args)\n",
    "fix_random(int(args.random_seed))\n",
    "\n",
    "save_path_attack = \"../..//record/\" + args.result_file_attack\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f959b510",
   "metadata": {},
   "source": [
    "### Step 2: Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8b67ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "loading...\n",
      "max_num_samples is given, use sample number limit now.\n",
      "subset bd dataset with length:  4995\n",
      "Create visualization dataset with \n",
      " \t Dataset: bd_test \n",
      " \t Number of samples: 4995  \n",
      " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# Load result\n",
    "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n",
    "selected_classes = np.arange(args.num_classes)\n",
    "\n",
    "# Select classes to visualize\n",
    "if args.num_classes>args.c_sub:\n",
    "    selected_classes = np.delete(selected_classes, args.target_class)\n",
    "    selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n",
    "    selected_classes = np.append(selected_classes, args.target_class)\n",
    "\n",
    "# keep the same transforms for train and test dataset for better visualization\n",
    "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n",
    "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n",
    "\n",
    "# Create dataset\n",
    "args.visual_dataset = 'bd_test'\n",
    "if args.visual_dataset == 'mixed':\n",
    "    bd_test_with_trans = result_attack[\"bd_test\"]\n",
    "    visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'clean_train':\n",
    "    clean_train_with_trans = result_attack[\"clean_train\"]\n",
    "    visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'clean_test':\n",
    "    clean_test_with_trans = result_attack[\"clean_test\"]\n",
    "    visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'bd_train':  \n",
    "    bd_train_with_trans = result_attack[\"bd_train\"]\n",
    "    visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'bd_test':\n",
    "    bd_test_with_trans = result_attack[\"bd_test\"]\n",
    "    visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n",
    "else:\n",
    "    assert False, \"Illegal vis_class\"\n",
    "\n",
    "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)}  \\n \\t Selected classes: {selected_classes}')\n",
    "\n",
    "# Create data loader\n",
    "data_loader = torch.utils.data.DataLoader(\n",
    "    visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n",
    ")\n",
    "\n",
    "# Create denormalization function\n",
    "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n",
    "    if isinstance(trans_t, transforms.Normalize):\n",
    "        denormalizer = get_dataset_denormalization(trans_t)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61034e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare background data\n",
    "num_bg = 200\n",
    "background_idx = np.random.choice(len(visual_dataset), num_bg, replace=False)\n",
    "background_samples = []\n",
    "for i in background_idx:\n",
    "    background_samples.append(visual_dataset[i][0].unsqueeze(0))\n",
    "    \n",
    "background_samples = torch.cat(background_samples, axis = 0).to(args.device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39104beb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number Poisoned samples: 4995\n",
      "Select 2 poisoned samples\n",
      "Select 2 clean samples\n"
     ]
    }
   ],
   "source": [
    "# Choose samples to show SHAP values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM\n",
    "total_num = 4\n",
    "bd_num = 0\n",
    "\n",
    "visual_samples = []\n",
    "visual_labels = []\n",
    "\n",
    "visual_poison_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset))\n",
    "if visual_poison_indicator.sum() > 0:\n",
    "    print(f'Number Poisoned samples: {visual_poison_indicator.sum()}')\n",
    "    # random choose two poisoned samples\n",
    "    selected_bd_idx = np.random.choice(np.where(visual_poison_indicator == 1)[0], 2, replace=False)\n",
    "    for i in selected_bd_idx:\n",
    "        visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n",
    "        visual_labels.append(visual_dataset[i][4])\n",
    "    bd_num = len(selected_bd_idx)\n",
    "    print(f'Select {bd_num} poisoned samples')\n",
    "        \n",
    "# Trun all samples to clean\n",
    "with temporary_all_clean(visual_dataset):\n",
    "    # you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples\n",
    "    selected_clean_idx = np.random.choice(len(visual_dataset), total_num-bd_num, replace=False)\n",
    "    for i in selected_clean_idx:\n",
    "        visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n",
    "        visual_labels.append(visual_dataset[i][1])\n",
    "    print(f'Select {len(selected_clean_idx)} clean samples')\n",
    "\n",
    "# Clean sample first\n",
    "visual_samples = visual_samples[::-1]\n",
    "visual_labels = visual_labels[::-1]\n",
    "\n",
    "visual_samples = torch.cat(visual_samples, axis = 0).to(args.device)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f652e5",
   "metadata": {},
   "source": [
    "### Step 3: Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff67e7b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load model preactresnet18 from badnet_demo\n"
     ]
    }
   ],
   "source": [
    "# Load model\n",
    "model_visual = generate_cls_model(args.model, args.num_classes)\n",
    "model_visual.load_state_dict(result_attack[\"model\"])\n",
    "model_visual.to(args.device)\n",
    "# !!! Important to set eval mode !!!\n",
    "model_visual.eval()\n",
    "print(f\"Load model {args.model} from {args.result_file_attack}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc952077",
   "metadata": {},
   "source": [
    "### Step 4: Plot SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94612903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting SHAP\n",
      "Choose layer layer4.1.conv2 from model preactresnet18\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x900 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "############## SHAP ##################\n",
    "print('Plotting SHAP')\n",
    "\n",
    "# Choose layer for feature extraction\n",
    "module_dict = dict(model_visual.named_modules())\n",
    "target_layer = module_dict[args.target_layer_name]\n",
    "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n",
    "\n",
    "sfm = torch.nn.Softmax(dim=1)\n",
    "outputs = model_visual(visual_samples)\n",
    "pre_p, pre_label = torch.max(sfm(outputs), dim=1)\n",
    "\n",
    "e = shap.GradientExplainer(\n",
    "    (model_visual, target_layer), background_samples, local_smoothing=0)\n",
    "shap_values, indexes = e.shap_values(visual_samples, ranked_outputs=5)\n",
    "\n",
    "# get the names for the classes\n",
    "class_names = np.array(args.class_names).reshape([-1])\n",
    "index_names = np.vectorize(\n",
    "    lambda x: class_names[x].capitalize())(indexes.cpu())\n",
    "# plot the explanations\n",
    "shap_numpy = [np.swapaxes(np.swapaxes(s, 1, -1), 1, 2) for s in shap_values]\n",
    "test_numpy = np.swapaxes(\n",
    "    np.swapaxes(denormalizer(visual_samples.cpu()).numpy(), 1, -1), 1, 2\n",
    ")\n",
    "test_numpy[test_numpy < 1e-12] = 1e-12  # for some numerical issue\n",
    "\n",
    "shap.image_plot(shap_numpy, test_numpy, index_names, show=False)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('py38')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12 (main, Apr  5 2022, 06:56:58) \n[GCC 7.5.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
